Mining Phase Research Articles

The Issue of the Use of Open Data and the Responsibility of Modern Journalists

Thanks to the large data source on the Internet, several improvements have also taken place in the working method of the professional journalist. Modern journalists may use software and data on the Internet as part of their news gathering operation. Combining the type of knowledge and data from the Internet with information from journalistic sources involved in the mining phase (authorities, police, and witnesses can carry unparalleled news capabilities) to a partner, Data journalist may also conduct a simple operation of linking and synthesizing between various documents in order to make new discoveries in the work process. This paper deals with the open data and the duty of the journalist to use it. The emergence of open data sources by governments around the world has made the search for data sources richer. Journalists may make use of open data to find knowledge. How do journalists make effective use of open data to create high-quality media products tailored to the needs of the modern public? We give the answer within the following text.

Mining sequential rules with itemset constraints

Mining sequential rules from a sequence database usually returns a set of rules with great cardinality. However, in real world applications, the end-users are often interested in a subset of sequential rules. Particularly, they may consider only rules that contain a specific set of items. The naive strategy is to apply such itemset constraints into the post-processing step. However, such approaches require much effort and time. This paper proposes the effective methods for integrating itemset constraints into the actual mining process. We proposed two algorithms, namely MSRIC-R and MSRIC-P, to solve this problem in which MSRIC-R pushed the constraints into the rule generating phase, and MSRIC-P pushes into the pattern mining phase. Experiments show that the proposed algorithms outperform the post-processing approach.

Development of an optimal experimental model for predicting rock mass rating based on tunneling quality index

In initial phase of mining and civil projects, rock mass rating (RMR) and tunneling quality index (Q) classification systems are widely used. Because of specifying these values, it is possible to predict various rock mass characteristics, such as geomechanical parameters. Hence, many researchers developed experimental models so that the RMR is obtained from Q. The predicted RMR is close to the real value in some domains of Q, but for some domains it is deviated from the real value. Therefore, designers are confused to choose the optimal results of the models. The purpose of this paper is to develop an optimal model for determining RMR various domains of Q. Hence, firstly, the performance of the previous models is evaluated in six different domains of Q and then the best models for each domain are selected. Using the simple regression method, an optimal experimental model is developed based on the selected model in each domain. For this purpose, 214 datasets in the available literatures are used. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) indicators are used to evaluate the performance of the models. Investigations show that the results of the developed model are more reliable than the other models for all of the domains. Therefore, the proposed models can be used as general models in all domains.

Nanoparticles in fossil and mineral fuel sectors and their impact on environment and human health: A review and perspective

Nanoscience and technology have enabled better insights into the environmental and health impacts arising from the mining, production and use of fossil and mineral fuels. Here we provide an overview of the nanoscience-based applications and discoveries concerning coal and mineral fuel (i.e., uranium-containing minerals) mining, refining/production, use, and disposal of wastes. These processes result in massive nanoparticle release and secondary nanoparticle generation which have highly significant environmental implications and human health consequences on local, regional, and even global levels. Until recently, very little was known about nanoparticle fractions. Recent advancements and sophistications enable us to detect, collect and study these materials which are roughly 1 nm (0.001 μm) up to several tens of nanometers in size. These materials are known to behave differently (chemically, electrically, and mechanically), relative to their macroscopic equivalents. This is what makes nanoscience fascinating and difficult to predict, underscoring the importance of this emerging new field. For example, nanoparticles associated with coal and mineral fuel influence the release, uptake, and transportation of hazardous elements associated with mining, processing, and waste storage in the surrounding areas. This includes long distance transport down streams, rivers, and eventually to oceans such as from coal and uranium mine drainages. In terms of human health, in all phases of mining, production/refining, use, and waste disposal, the associated nanoparticles can be acquired through oral ingestion, inhalation, and dermal absorption. Inhalation has been shown to be particularly damaging, where lung, heart, kidney, and brain diseases are prevalent.Relative to all other fields of science and engineering associated with coal and mineral fuel mining, production, use, and clean-up efforts, nanoscience, although a much newer field then the rest by comparison, is still greatly under-represented and under-utilized. There is also a continuing gap between what we so far know about the behavior of nanoparticles, and what remains to be discovered.

Teaching-learning-based pathfinder algorithm for function and engineering optimization problems

Pathfinder algorithm (PFA) for finding the best food area or prey based on the leadership of collective action in animal groups is a new metaheuristic algorithm for solving optimization problems with different structures. PFA is divided into two stages to search: pathfinder stage and follower stage. They represent the exploration phase and mining phase of PFA respectively. However, the original algorithm also has the problem of falling into a local optimum. In order to solve this problem, the teaching phase in the teaching and learning algorithm is added to the pathfinder stage in the text. In order to balance the exploration and mining capabilities of the algorithm, the learning phase of the teaching and learning algorithm is added to the follower phase in the article. In order to further enhance the depth search ability of the algorithm and increase the convergence speed, the exponential step is given to the followers. Therefore, a teaching-learning-based pathfinder algorithm (TLPFA) is proposed. 19 benchmark functions of four different types and six engineering design problems are used to test of the TLPFA exploration and exploiting capabilities. The experimental results show that the proposed TLPFA algorithm is superior to the state-of-the-art metaheuristic algorithms in terms of the performance measures.

Assessment of the Ecological Impacts of Coal Mining and Restoration in Alpine Areas: A Case Study of the Muli Coalfield on the Qinghai-Tibet Plateau

Surface mining inevitably impacts the ecological environment, especially in alpine and fragile mining areas. Thus, it is worth discussing the dynamic impact process of mining on regional land use and landscapes. In this study, we took the Muli coalfield, a typical alpine mining area on the Qinghai-Tibet Plateau (QTP), as an example and analyzed the changing characteristics of the eco-environment in 2003-2010 (low-intensity mining), 2010-2014 (large-scale mining), and 2014-2020 (restoration) through a land use transfer matrix, landscape fragmentation index and vegetation cover based on remote sensing data, and discussed the problems and lessons learned. The results show that: (1) the land use transfer process changed from active to basically stable. During the mining phase, the land use transfers were complex and mainly consisted of the transfers of grassland to production land. The land use transfers during the restoration phase were not obvious. (2) The natural landscape types (e. g., river and grassland) were separated by surface mining. In terms of landscape fragmentation, the patch densities showed an increasing trend, while the patch shape fragmentation index showed first decreased and then increased. Additionally, the level of landscape fragmentation during the restoration phase did not change significantly. (3) The changes in average vegetation cover in the gangue fields occurred in four stages: i) gradually decreases from 2003 to 2010, ii) rapid decreases from 2010 to 2014, iii) increases from 2014 to 2017, and iv) further decreases again after 2017. (4) We summarized the lessons learned from the mining and restoration processes and provided a reference for addressing the conflict between mineral exploitation and environmental protection in ecologically fragile alpine mining areas.

Providing A Model for Predicting and Detecting Destructive Processes to Prevent the Production of Waste and Defective Products: A Data Mining Approach

Today, most industries use statistical quality control tools to improve quality and reduce the defective products and waste, but the high volume of data requires the help of a powerful tool to control processes. One of the objectives of the present study is to predict defective products and prevent their production using data mining tools due to the high power in data analysis and its predictive nature, which is less used in the industry. In this study, the statistical population of all parts produced in 2017 by Shabrun Company. The statistical sample is 2400 pieces of radiators that were randomly selected from the production line. In the operational phases of data mining, three decision tree algorithms were used: C&R Tree, Quest Tree and Chaid Tree. Using these algorithms, the most important criteria affecting quality control and rules leading to the quality of parts were determined. Comparative results showed that despite the validity of all three algorithms, the C&R Tree algorithm had the highest accuracy. Adherence to the rules resulting from the implementation of these algorithms has led to the detection and prevention of waste generation, which has increased efficiency and prevented the loss of time and cost in this production unit.

Moving towards resource efficiency and circular economy in the brick manufacturing sector in Zimbabwe

The life cycle assessment of the ABC (Pvt) Ltd brick manufacturing plant has considered land use, fossil resource scarcity, water consumption, global warming and fine particulate matter formation as the impact categories for assessment, with clay mining and coal as the input flows with the highest significant contributions to environmental load. The phase of clay mining (65.8%) is significantly impacting on all the investigated impact categories followed by brick moulding (24.8%) and brick roasting (9.4%) phases, respectively. Hotspots were assessed to identify potential for resource efficiency and circular economy at ABC bricks, Zimbabwe. It can be concluded that ABC is severely polluting the air with emissions above the Environmental Management Agency (EMA) standards for SO2, CO, PM and NOx thus putting kiln workers at risk of respiratory diseases. The calculated Air Quality Index (AQI) ranks CO as the most affecting pollutant with an average score of ∼600. Clay production efficiency was also determined, and an analysis revealed that extrusion and clamping stage contributed highly to the clay losses during brick moulding. Therefore, focus must be placed on these process steps to reduce raw material losses. Furthermore, an environmental waste (fly ash) was used in different weight percentage ratios of 10%, 20% and 100% to substitute clay. The increase of the fly ash content in the brick making process proved to significantly reduce the environmental load among the selected impact categories. ABC uses clay as its main raw material hence the high demand for clay. Strategies should include accounting of used clay daily and raw materials substitution. If ABC uses fly ash from its brick kilns and from other thermal power plant boilers to mix with clay in brick production, then the quantity of clay demanded will be reduced. Using fly ash will reduce rate of clay extraction while at the same time solving the problem of fly ash disposal in Zimbabwe. This circular option will ultimately result in reduced pit expansion, hence reducing top-soil loss and environmental degradation. It should not be disregarded that top-soil loss in turn affects food security. By adopting appropriate technologies, implementing resource efficiency, and designing circular economy patterns, the brick manufacturing sector in Zimbabwe may not only reduce production waste but also comply with enforced environmental protection legislation.

Óbitos infantis entre os anos 2000 e 2017 em uma cidade do sul do Brasil: técnicas de mineração de dados

Objetivo: Analisar padrões de óbitos infantis entre os anos 2000 e 2017 em uma cidade do sul do Brasil. Método: A partir da extração de dados da base de mortalidade infantil do Departamento de Informática do Sistema Único de Saúde (DATASUS), aplicar técnicas de análise de dados baseados no método de Descoberta de informações, incorporando, na fase de mineração de dados, algoritmos de aprendizado de máquina provindos do software WEKA. Resultados: O estudo evidencia a associação entre o baixo peso ao nascer e reforça a sua manutenção como critério isolado para a identificação de crianças vulneráveis ao óbito em menores de um ano. Apesar das lacunas encontradas na base de dados do sistema de bases de mortalidade, evidencia que esta é uma potencial fonte de informação para a detecção de fatores associados à mortalidade infantil. Considerações Finais: Algoritmos computacionais são eficazes para realizar associações, agrupamentos e previsões de maneira eficaz em dados de saúde, no entanto, a base de dados necessita ser alimentada com dados confiáveis regularmente.

Anthropogenic and climate controls on vegetation changes between 1500 BCE and 500 CE reconstructed from a high-resolution pollen record from varved sediments of Lake Mondsee, Austria

This study reports a precisely dated pollen record with a 20-year resolution from the varved sediments of Lake Mondsee in the north-eastern European Alps (47°49′N, 13°24′E, 481 m above sea level). The analysed part of core spans the interval between 1500 BCE and 500 CE and allows changes in vegetation composition in relation to climatic changes and human activities in the catchment to be inferred. Intervals of distinct but modest human impact are identified at ca. 1450–1220, 740–490 and 340–190 BCE and from 80 BCE to 180 CE. While the first two intervals are synchronous with prominent salt mining phases during the Bronze Age and Early Iron Age at the nearby UNESCO World Heritage Site of Hallstatt, the last two intervals fall within the Late Iron Age and Roman Imperial Era, respectively. Comparison with published records of extreme runoff events obtained from the same sediment core shows that human activities (including agriculture and logging) around Lake Mondsee were low during intervals of high flood frequency as indicated by a higher number of intercalated detrital event layers, but intensified during hydrologically stable intervals. Comparison of the pollen percentages of arboreal taxa with the stable oxygen isotope and potassium ion records of the NGRIP and GISP2 ice cores from Greenland reveals significant positive correlations for Fagus and negative correlations for Betula and Alnus. This underlines the sensitivity of vegetation around Lake Mondsee to temperature fluctuations in the North Atlantic as well as to moisture fluctuations controlled by changes in the intensity of the Siberian High and the North Atlantic Oscillation (NAO) regime.

Exploitation, post-mining, re-exploration? New projects for former French metal mines

The prospect of a mining revival in metropolitan France emerged in recent years, following the European Union's recommandations towards mining independence. Since 2013, several exploration permits have been issued, while demands are still pending. Some of the targeted areas include old mines that were industrially exploited previously. After the mining and post-mining periods, current re-exploration projects could open a third phase in the socio-environmental trajectories of French mines, marked by the resumption of exploitation. However, re-exploitation appears uncertain considering the controversies generated by mobilizations of opponents on a vast majority of the concerned sites. How does the prospect of a mining revival redirect the reading of the socio-environmental trajectories of former metal mines, including the past and present phases of mining and post-mining? This article will show (i) that the re-exploration of former metal mines is a new issue, being part of a renewed interest in the potential resources of the French subsoil, (ii) that re-exploration projects bring to the surface the ambivalent memory of a mining past that was relatively buried, and (iii) that these projects and the controversies they generate lead to new readings of this mining past, arousing awareness of environmental impacts and post-mining mismanagement.

Groundwater environmental risk assessment of abandoned coal mine in each phase of the mine life cycle: a case study of Hongshan coal mine, North China.

Human activities during each phase of coal mine life cycle greatly affect groundwater environment. The groundwater environment destruction is not just only the destruction of underground structure but also the social problems caused by available groundwater resources reduction, as well as the environmental problems affecting ecosystem and human health. Moreover, the groundwater environmental risk of coal mining is complex, dynamic, and long-term. Therefore, a framework and quantitative method for groundwater environmental risk analysis at different phases of the mine life cycle was presented, which is composed of the groundwater system destruction risk (GSDR) and social-economic-ecological vulnerability (SEEV) assessment. The framework was applied in Hongshan abandoned coal mine, North China. Based on the aquifer structure destruction, groundwater flow field evolution, contamination, and social-economic influence analysis, 12 main controlling factors for the GSDR and 7 factors for the SEEV were determined and quantified separately. The results showed that the groundwater contamination of the Hongshan mine mainly occurred after closure, caused by the cross-strata pollution of mine water, which significantly reduced the groundwater available resources, which greatly affected local social-economy sustainable development and residents' health. The Hongshan mine closure increased groundwater environmental risk, with the GSDR high-risk zone being 12.51km2 larger than that during the mining phase and the SEEV was calculated at a high level. This framework promotes systematic integration of the groundwater environmental risk assessment in mine life cycle.

UWFP-Outlier: an efficient frequent-pattern-based outlier detection method for uncertain weighted data streams

In this paper, we propose an efficient frequent-pattern-based outlier detection method, namely, UWFP-Outlier, for identifying the implicit outliers from uncertain weighted data streams. For reducing the time cost of the UWFP-Outlier method, in the weighted frequent pattern mining phase, we introduce the concepts of the maximal weight and maximal probability to form a compact anti-monotonic property, thereby reducing the scale of potential extensible patterns. For accurately detecting the outliers, in the outlier detection phase, we design two deviation indices to measure the deviation degree of each transaction in the uncertain weighted data streams by considering more factors that may influence its deviation degree; then, the transactions which have large deviation degrees are judged as outliers. The experimental results indicate that the proposed UWFP-Outlier method can accurately detect the outliers from uncertain weighted data streams with a lower time cost.

DCE -miner: an association rule mining algorithm for multimedia based on the MapReduce framework

The amount of multimedia data has grown rapidly because of improvements in data collection and storage technologies. The association rule mining (ARM) technique is a type of data mining method widely used to extract useful information from data warehouses. In real-world big data applications, fast and effective data mining algorithms are emerging as a valuable approach. In this paper, we propose DCE-Miner, a fast association rule mining algorithm with low memory requirements based on the MapReduce framework. In the precomputation phase, we split large datasets into equal-sized smaller ones using data division method. In the frequent K-itemsets mining phase, the mappers read the small datasets and distribute the data to reducers based on the closed set characteristics associated with each partition. The reducers use bitmaps to accelerate the computation speed and store the possible frequent 2-itemsets to reduce future computation. Extensive experimental results show that on large-scale datasets with up to 40 million transactions, DCE-Miner achieves better performance and is more robust with respect to dataset sizes and support level than are the current algorithms.

Top-[formula omitted] closed co-occurrence patterns mining with differential privacy over multiple streams

The frequent pattern mining over data streams is a very important problem for many applications. However, many researches investigate a single stream in which every transaction is independent and it is not considered that some transactions are generated by the same individual. Some real-world applications involve multiple streams that continuously generate objects, and interesting observations are the objects appearing in many streams, such as emerging topic discovery, e-commerce, web usage pattern mining and location-based services. In this paper, we analyze the privacy problems in mining top-k closed co-occurrence patterns over multiple streams caused by single release of a window and continuous releases in successive windows. To prevent privacy leakage, we propose a differentially private top-k closed co-occurrence patterns mining algorithm across multiple streams with exponential mechanism and Laplace mechanism. The algorithm consists of dissimilarity calculation phase and differentially private mining phase, where differentially private mining phase includes adjusting CP-Graph with splitting transaction, perturbing CP-Graph to obtain the top-k closed co-occurrence patterns candidate set and adding noise to the supports of patterns. Finally, we prove our algorithm satisfies differential privacy and experiment results show the utility and efficiency of our algorithm.

The Recognition of Brazilian Baiano and Gaucho Regional Dialects on Twitter Using Text Mining

The internet has broken geographical barriers and brought people and cultures closer independent to their physical location. However, the language, idiom, dialects and accents continue to characterize individuals in their origins. The Brazilian regional dialect is the object of study of this research, which deals with linguistic corpora analyzed from a volume of data extracted on Twitter. This paper presents the results of the mining phase that makes up a first stage of the project to create a technique for recognizing the Brazilian Portuguese regional dialects. Analysis and conclusions were be made only for the baiano and gaucho dialects, considering the significant size of the samples and the need to reach a diagnosis of the collect data set.

Building a respiratory diseases recognition system Using Data mining techniques

Respiratory diseases are among the most common causes of morbidity and mortality worldwide. Computer-assisted learning tools have been shown to promote self-directed learning and enhance problem-solving and predicting skills. However, few have been developed in the area of respiratory medicine. Thus, we aimed in this study to build a framework to predict the respiratory system diseases by applying Data Mining Techniques (DMT) on clinical information. The framework composed of three main phases which are preprocessing phase, data mining phase and evaluation phase to evaluate the built prediction model. This Approach statistics shows acceptable results.

Innovation potential along the phosphorus supply chain: A micro and macro perspective on the mining phase

Phosphorus is unique, given its characteristic of being essential for all life on Earth. The element is non-substitutable and finite in the form of highly concentrated phosphate-rock deposits. Thus, humankind should strive to utilize this resource in the most-efficient and sustainable manner. Losses, as well as overlooked opportunities, can be found all along the supply chain in various forms and to distinct extents. Avoiding these by closing the loops all along the supply chain is a key approach for keeping phosphorus flows available for economic use while reducing negative environmental impacts such as eutrophication. Changes to the current, mostly linear approach require multidimensional innovations that address products, processes, structures, and decision-makers along the supply chain as well as societal stakeholders. Our work focuses on the mining phase, covering extraction and beneficiation, whereby we discuss innovation potential in the contexts of i) improving P2O5 recovery, ii) utilizing waste, and iii) recovering by-products within the boundaries of sustainable development as generalized strategies on the macro level. Furthermore, we show that there is no “one-size-fits-all” solution to overcome current and future challenges within phosphate-rock mining, as the geological composition and processing of ores differs fundamentally among global deposits. Therefore, we perform, based on previously unpublished primary data, an economic breakdown of production-cost structures covering 85 active phosphate-rock mines and show significant differences between the two main deposit types (i.e., igneous and sedimentary), underground and open-pit mining as well as within each type.

Improving symbolic data visualization for pattern recognition and knowledge discovery

This paper examines the visualization of symbolic data and considers the challenges rising from its complex structure. Symbolic data is usually aggregated from large data sets and used to hide entry specific details and to transform huge amounts of data (like big data) into analyzable quantities. It is also used to offer an overview in places where general trends are more important than individual details. Symbolic data comes in many forms like intervals, histograms, categories and modal multi-valued objects. Symbolic data can also be considered as a distribution. Currently, the de facto visualization approach for symbolic data is zoomstars which has many limitations. The biggest limitation is that the default distributions (histograms) are not supported in 2D as additional dimension is required. This paper proposes several new improvements for zoomstars which would enable it to visualize histograms in 2D by using a quantile or an equivalent interval approach. In addition, several improvements for categorical and modal variables are proposed for a clearer indication of presented categories. Recommendations for different approaches to zoomstars are offered depending on the data type and the desired goal. Furthermore, an alternative approach that allows visualizing the whole data set in comprehensive table-like graph, called shape encoding, is proposed. These visualizations and their usefulness are verified with three symbolic data sets in exploratory data mining phase to identify trends, similar objects and important features, detecting outliers and discrepancies in the data.

Geotechnical Engineering for Mass Mining

Editor’s note: The Geology and Mining series, edited by Dan Wood and Jeffrey Hedenquist, is designed to introduce early-career professionals and students to a variety of topics in mineral exploration, development, and mining, in order to provide insight into the many ways in which geoscientists contribute to the mineral industry. Abstract The rock mass response to mining is governed by the rock mass characteristics and the mining-induced changes that drive its behavior. To be able to study and accurately predict the response of the rock mass to mining, it is imperative that both the orebody and the enclosing country rocks are well characterized through the collection and analysis of large quantities of good-quality, representative geologic, structural, geotechnical, and hydrogeological data. These are the fundamental constituents of a good geotechnical model whose reliability improves as the mining project matures and moves from exploration and study phases, passes the decision to develop, and proceeds into construction and then operations. Each phase provides greater exposure to the rock mass, reduces uncertainty, and increases reliability in the geotechnical model and in an understanding of the rock mass behavior. The quest of the geotechnical engineer is to understand the rock mass behavior and is no different from that of the geologist who defines the mineral resource, and it warrants (at the very least) the same level of rigor in data collection, analysis, and reporting. Just as the geologist continues to improve the orebody model through grade reconciliation during mining, so the geotechnical engineer must continually revisit and calibrate the geotechnical model during the operational phase of mining through geotechnical monitoring. The increasing demand by investors and stakeholders that the performance of a mine does not deviate from plan due to unforeseen geotechnical surprises warrants a significant shift in the level of geotechnical data collection, analyses, and rock mass monitoring through all stages of study and operations. This demand warrants supporting budgets and assurance processes that are commensurate with the complexity and extent of the geotechnical uncertainties.